引入极线约束的SURF特征匹配算法

目的 特征点匹配算法是当今计算机图像处理领域的研究热点,但是大多数现存的方法不能同时获得数量多和质量优的匹配。鉴于此,基于SURF （speeded-up robust features）算法,通过引入极线约束来提高特征匹配效果。方法 首先使用SURF算法检测和描述图像特征点,然后使用RANSAC （random sampling consensus）方法计算匹配图像之间的基础矩阵,通过该基础矩阵计算所有特征点的极线。再引入极线约束过滤掉错误匹配,最终获得数量与质量显著提高的匹配集合。结果 实验结果表明,该方法获得的匹配具有高准确度,匹配数目与原约束条件相比可高达2～8倍。结论 本文方法实现过程简单,不仅匹配准确度高且能够大大提高正确的特征匹配数,适用于处理不同类型的图像数据。     

基于遗传算法不同策略下的基础矩阵估计方法

在未定标系统中，对极几何约束给出了图像间的全部信息，成为解决许多视觉问题的关键环节，提出了一种基于遗传算法不同策略下的基础矩阵估计方法，它利用每个基因代表一个匹配点，每条染色体作为基础矩阵计算时的最小子集，并根据染色体长度决定采用何种策略估计基础矩阵，此方法在很大程度上减小了出格点对估计过程的影响，能够较好地汇聚到全局最优解，模拟数据和真实图像的实验结果都表明，所给出的方法能够有效地检测和删除错定位和误匹配点，提高了基础矩阵估计的鲁棒性和精度。     

一种用于三维重建的彩色Sift准稠密匹配算法

针对复杂光照条件下Sift算法对彩色图像匹配能力较差,基于Kubelka-Munk理论,提出了一种适用于未标定图像的准稠密立体匹配算法,有助于更精确地进行三维重建。该算法首先求出彩色图像各个像素的颜色不变量,提取彩色特征点并通过构造彩色Sift特征描述子进行初匹配,采用RANSAC鲁棒算法消除误匹配生成种子点;然后依据视差约束提出一种基于视差梯度均值自适应窗口方法,根据视差梯度均值调整搜索范围;最后采用最优先原则进行区域增长。实验证明,该算法能获得比较满意的匹配效果,是一种有效的用于三维重建的准稠密匹配算法。     

一种新的高精度的L-M基本矩阵估计算法

分析了基于随机抽样检测思想的现有鲁棒算法在基本矩阵估计中存在的不足,结合LMedS和M估计法各自的优点,提出一种新的高精度的L-M基本矩阵估计算法。利用LMedS思想方法获得内点集,此时内点集通常情况下不包含误匹配,但仍存在位置误差,用Torr-M估计法计算基本矩阵,因为当匹配点只存在位置误差时,用M估计法得到的基本矩阵非常精确。大量的模拟实验和真实图像实验数据表明,在高斯噪声和误匹配存在的情况下,该算法具有更高的鲁棒性和精确度。     

一种半自动的三维欧氏重建方法

从二维图像得到场景的三维模型是计算机视觉和虚拟现实的重要研究内容。本文通过用户的简单交互,利用平面型场景的同形关系自动进行两幅大差异图像之间的角点匹配,将这些匹配结果作为初始点,再利用RANSAC鲁棒算法估计基本矩阵。以此结果进行仿射重建,然后在简化相机模型的基础上通过给出的约束条件直接实现欧氏重建,真实数据的实验结果表明了该算法的有效性。     

Robust multi-scale image matching for deriving ice surface velocity field from sequential satellite images

The cross-correlation-based image matching method has been widely used to derive ice surface motion information from sequential satellite images through tracking spatial displacements of surface features over time. However, this conventional method is not adequate for handling areas with a high velocity variation, in which case a large search window has to be specified in order to find the correct match points for fast-moving features. The computation involved in the cross-correlation matching with a large search window is often so intensive that only a sparse set of velocity measurements can be attempted. Furthermore, with a greater search window the cross-correlation method is prone to more spurious matches. This article presents a robust multi-scale image matching method to address the deficiencies of the conventional cross-correlation technique. The main idea is to use approximate match points obtained at a coarse resolution as a guide for searching for more precise matches at a higher resolution. A robust local statistical operator is embedded at each scale in the multi-scale matching process to eliminate match outliers. The strategy of progressively refining the match precision from coarse-resolution images to the full-resolution image allows for a small search range in pixels. Our robust multi-scale matching method significantly speeds up the computation and also reduces the occurrences of bad and spurious match points. We have implemented our method as a software tool with a graphical user interface and successfully applied it to process sequential Radarsat synthetic aperture radar images for extracting high-resolution velocity fields for Antarctic glaciers and ice shelves. This software tool will be freely available to the public through the Internet.     

N-dimensional tensor voting and application to epipolar geometryestimation

We address the problem of epipolar geometry estimation by formulating it as one of hyperplane inference from a sparse and noisy point set in an 8D space. Given a set of noisy point correspondences in two images of a static scene without correspondences, even in the presence of moving objects, our method extracts good matches and rejects outliers. The methodology is novel and unconventional, since, unlike most other methods optimizing certain scalar, objective functions, our approach does not involve initialization or any iterative search in the parameter space. Therefore, it is free of the problem of local optima or poor convergence. Further, since no search is involved, it is unnecessary to impose simplifying assumption to the scene being analyzed for reducing the search complexity. Subject to the general epipolar constraint only, we detect wrong matches by a computation scheme, 8D tensor voting, which is an instance of the more general N-dimensional tensor voting framework. In essence, the input set of matches is first transformed into a sparse 8D point set. Dense, 8D tensor kernels are then used to vote for the most salient hyperplane that captures all inliers inherent in the input. With this filtered set of matches, the normalized eight-point algorithm can be used to estimate the fundamental matrix accurately. By making use of efficient data structure and locality, our method is both time and space efficient despite the higher dimensionality. We demonstrate the general usefulness of our method using example image pairs for aerial image analysis, with widely different views, and from nonstatic 3D scenes. Each example contains a considerable number of wrong matches     

In defense of the eight-point algorithm

The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the eight-point algorithm is a frequently cited method for computing the fundamental matrix from a set of eight or more point matches. It has the advantage of simplicity of implementation. The prevailing view is, however, that it is extremely susceptible to noise and hence virtually useless for most purposes. This paper challenges that view, by showing that by preceding the algorithm with a very simple normalization (translation and scaling) of the coordinates of the matched points, results are obtained comparable with the best iterative algorithms. This improved performance is justified by theory and verified by extensive experiments on real images     

A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images

According to the World Health Organization, breast cancer is the most common form of cancer in women. It is the second leading cause of death among women round the world, becoming the most fatal form of cancer. Despite the existence of several imaging techniques useful to aid at the diagnosis of breast cancer, x-ray mammography is still the most used and effective imaging technology. Consequently, mammographic image segmentation is a fundamental task to support image analysis and diagnosis, taking into account shape analysis of mammary lesions and their borders. However, mammogram segmentation is a very hard process, once it is highly dependent on the types of mammary tissues. The GrowCut algorithm is a relatively new method to perform general image segmentation based on the selection of just a few points inside and outside the region of interest, reaching good results at difficult segmentation cases when these points are correctly selected. In this work we present a new semi-supervised segmentation algorithm based on the modification of the GrowCut algorithm to perform automatic mammographic image segmentation once a region of interest is selected by a specialist. In our proposal, we used fuzzy Gaussian membership functions to modify the evolution rule of the original GrowCut algorithm, in order to estimate the uncertainty of a pixel being object or background. The main impact of the proposed method is the significant reduction of expert effort in the initialization of seed points of GrowCut to perform accurate segmentation, once it removes the need of selection of background seeds. Furthermore, the proposed method is robust to wrong seed positioning and can be extended to other seed based techniques. These characteristics have impact on expert and intelligent systems, once it helps to develop a segmentation method with lower required specialist knowledge, being robust and as efficient as state of the art techniques. We also constructed an automatic point selection process based on the simulated annealing optimization method, avoiding the need of human intervention. The proposed approach was qualitatively compared with other state-of-the-art segmentation techniques, considering the shape of segmented regions. In order to validate our proposal, we built an image classifier using a classical multilayer perceptron. We used Zernike moments to extract segmented image features. This analysis employed 685 mammograms from IRMA breast cancer database, using fat and fibroid tissues. Results show that the proposed technique could achieve a classification rate of 91.28% for fat tissues, evidencing the feasibility of our approach.     

Robust feature matching via Gaussian field criterion for remote sensing image registration

Feature matching, which refers to establishing reliable feature correspondences between two images of the same scene, is a critical prerequisite in a wide range of remote sensing tasks including environment monitoring, multispectral image fusion, image mosaic, change detection, map updating. In this paper, we propose a method for robust feature matching and apply it to the problem of remote sensing image registration. We start by creating a set of putative feature matches which can contain a number of unknown false matches, and then focus on mismatch removal. This is formulated as a robust regression problem, and we customize a robust estimator, namely the Gaussian field criterion, to solve it. The robust criterion can handle both linear and nonlinear image transformations. In the linear case, we use a general homography to model the transformation, while in the nonlinear case, the non-rigid functions located in a reproducing kernel Hilbert space are considered, and a regularization term is added to the objective function to ensure its well-posedness. Moreover, we apply a sparse approximation to the non-rigid transformation and reduce the computational complexity from cubic to linear. Extensive experiments on various natural and remote sensing images show the effectiveness of our approach, which is able to yield superior results compared to other state-of-the-art methods.     

三视校正的理论及鲁棒性算法

主要讨论两方面的工作.首先,对三视校正问题进行理论分析,给出了校正后图像的基本矩阵与其约束条件之间的关系,讨论了三视校正过程中的6个自由参数的几何含义.这些结果为处理校正过程中带来的图像射影畸变提供了理论根据.其次,在RANSAC(random sampling consensus)框架下,提出了一种鲁棒的三视校正算法.与传统的校正算法不同,该算法不再只依赖于基本矩阵,而是直接利用了原始匹配点的信息.这种基于点的方法有两个优点:一方面,由于噪声的干扰,基本矩阵往往估计得不够准确;另一方面,由于基本矩阵的评价准则与校正结果的评价准则不同,即使从好的基本矩阵出发,也未必能获得好的校正结果.大量的模拟和真实图像实验表明,该算法具有很强的抗噪声及抗错误匹配的能力,能够获得令人满意的校正效果.     

An adaptive semi-supervised Fuzzy GrowCut algorithm to segment masses of regions of interest of mammographic images

According to the World Health Organization, breast cancer is the most common cancer in women worldwide, becoming one of the most fatal types of cancer. Mammography image analysis is still the most effective imaging technology for breast cancer diagnosis, which is based on texture and shape analysis of mammary lesions. The GrowCut algorithm is a general-purpose segmentation method based on cellular automata, able to perform relatively accurate segmentation through the adequate selection of internal and external seed points. In this work we propose an adaptive semi-supervised version of the GrowCut algorithm, based on the modification of the automaton evolution rule by adding a Gaussian fuzzy membership function in order to model non-defined borders. In our proposal, manual selection of seed points of the suspicious lesion is changed by a semiautomatic stage, where just the internal points are selected by using a differential evolution algorithm. We evaluated our proposal using 57 lesion images obtained from MiniMIAS database. Results were compared with the semi-supervised state-of-the-art approaches BEMD, BMCS, Wavelet Analysis, LBI, Topographic Approach and MCW. Results show that our method achieves better results for circumscribed, spiculated lesions and ill-defined lesions, considering the similarity between segmentation results and ground-truth images.     

Feature-Based Deformable Surface Detection with Self-Occlusion Reasoning

This paper presents a method for detecting a textured deformed surface in an image. It uses (wide-baseline) point matches between a template and the input image. The main contribution of the paper is twofold. First, we propose a robust method based on local surface smoothness capable of discarding outliers from the set of point matches. Our method handles large proportions of outliers (beyond 70% with less than 15% of false positives) even when the surface self-occludes. Second, we propose a method to estimate a self-occlusion resistant warp from point matches. Our method allows us to realistically retexture the input image. A pixel-based (direct) registration approach is also proposed. Bootstrapped by our robust point-based method, it finely tunes the warp parameters using the value (intensity or color) of all the visible surface pixels. The proposed framework was tested with simulated and real data. Convincing results are shown for the detection and retexturing of deformed surfaces in challenging images.     

基于改进SIFT的离线签名鉴别方法

针对高质量模仿签名和签名图像的尺度变化对鉴别效果影响较大的问题,提出了一种基于改进SIFT的离线签名鉴别方法。该方法首先改进尺度空间的建立,减少尺度空间的层数和组数,然后检测签名图像的SIFT特征点和提取特征描述子,根据特征描述子间的欧氏距离进行匹配,通过邻近距离之比和特征点角度差筛选匹配对,并对匹配对特征点的角度差进行直方图统计构成ODH(Orientation Difference Histogram)特征向量。最终根据匹配对的数量和ODH特征向量的相似度完成鉴别工作。本文方法在本地数据库上的等误率为6.7%,在4NSigComp2010公共数据库上的等误率为20%。实验结果表明,该方法与现有方法相比有效的提高了鉴别正确率。     

Direct linear sub-pixel correlation by incorporation of neighbor pixels' information and robust estimation of window transformation

Standard methods for sub-pixel matching are iterative and nonlinear; they are also sensitive to false initialization and window deformation. In this paper, we present a linear method that incorporates information from neighboring pixels. Two algorithms are presented: one ‘fast’ and one ‘robust’. They both start from an initial rough estimate of the matching. The fast one is suitable for pairs of images requiring negligible window deformation. The robust method is slower but more general and more precise. It eliminates false matches in the initialization by using robust estimation of the local affine deformation. The first algorithm attains an accuracy of 0.05 pixels for interest points and 0.06 for random points in the translational case. For the general case, if the deformation is small, the second method gives an accuracy of 0.05 pixels; while for large deformation, it gives an accuracy of about 0.06 pixels for points of interest and 0.10 pixels for random points. They are very few false matches in all cases, even if there are many in the initialization. Received: 24 July 1997 / Accepted: 4 December 1997     

一种基于兴趣点方向特征的图像拼接方法

提出了一种基于兴趣点方向特征的图像拼接算法IPOF(Interest Point Orientation Feature),该算法利用Harris角检测器提取出两幅图像的兴趣点并为每个兴趣点分配一个主方向,采用方向相关系数法提取出初始匹配对,根据特征点之间的关系去除伪匹配对,得到两幅图像的对应兴趣点特征对从而确定变换参数,最后使用加权平均的方法融合图像。实验表明,该算法在图像间存在任意角度的旋转及平移的情形下,能有效地实现图像的平滑镶嵌。     

Camera–projector matching using unstructured video

This paper presents a novel approach for matching 2-D points between a video projector and a digital camera. Our method is motivated by camera–projector applications for which the projected image needs to be warped to prevent geometric distortion. Since the warping process often needs geometric information on the 3-D scene obtained from a triangulation, we propose a technique for matching points in the projector to points in the camera based on arbitrary video sequences. The novelty of our method lies in the fact that it does not require the use of pre-designed structured light patterns as is usually the case. The backbone of our application lies in a function that matches activity patterns instead of colors. This makes our method robust to pose, severe photometric and geometric distortions. It also does not require calibration of the color response curve of the camera–projector system. We present quantitative and qualitative results with synthetic and real-life examples, and compare the proposed method with the scale invariant feature transform (SIFT) method and with a state-of-the-art structured light technique. We show that our method performs almost as well as structured light methods and significantly outperforms SIFT when the contrast of the video captured by the camera is degraded.     

基于区域生长的多源遥感图像配准

多源遥感图像由于成像设备、所用光谱、拍摄时间等因素的不同,给配准带来极大的困难.尽管已经提出了多种匹配方法,但已有方法一般只能适用于特定的应用环境,开发出更加稳定和适用的配准算法仍然是一个极具挑战性的研究课题.提出一种基于区域生长的配准方法,首先,提取改进后的尺度不变特征,通过全局匹配确定种子点和种子区域并完成变换模型的初始化;然后,运用迭代区域生长和双向匹配策略,得到整个图像的可靠匹配点,从而实现多源遥感图像之间的配准.实验表明,该方法提取的匹配点的数量和正确率均远高于已有方法,能够对存在严重灰度差异的多源遥感图像实现高精度的配准,充分证明了该方法的鲁棒性和适用性.